However, the lack of a large-scale annotated dataset is one of the most frequently mentioned obstacles that hinder further improvement.
In practice, this limits the generalizeability of supervised learning systems, as will be discussed in Section ,
and using synthetic data is one way to address this issue [1]}, [2]}. Although there have been proposals to use unlabeled data [3]}, [4]}, the issue remains as they still rely on supervised learning combined with teacher-student learning [5]}. Parallel to those approaches, an annotation-free and, therefore, a more scalable and generalizable alternative would be unsupervised learning.
